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burrito_integrity_check

Predict burrito structural failure during consumption using tortilla stress models based on ingredient amounts like rice, beans, salsas, and proteins.

Instructions

Uses proprietary tortilla stress models to predict whether your burrito will structurally fail during consumption.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
riceYesRice amount
beansYesBeans amount
salsasYesWhich salsas are included
sour_creamYesSour cream included?
guacNoGuacamole included?
double_proteinNoDouble protein?

Implementation Reference

  • The 'burrito_integrity_check' tool implementation using 'server.tool' to register the tool and define its handler logic, which calculates burrito structural failure probability based on ingredients.
    server.tool(
      "burrito_integrity_check",
      "Uses proprietary tortilla stress models to predict whether your burrito will structurally fail during consumption.",
      {
        rice: z.enum(["normal", "extra", "none"]).describe("Rice amount"),
        beans: z.enum(["normal", "extra", "none"]).describe("Beans amount"),
        salsas: z
          .array(z.enum(["pico", "green", "red", "corn", "none"]))
          .describe("Which salsas are included"),
        sour_cream: z.boolean().describe("Sour cream included?"),
        guac: z.boolean().default(false).describe("Guacamole included?"),
        double_protein: z.boolean().default(false).describe("Double protein?"),
      },
      async ({ rice, beans, salsas, sour_cream, guac, double_protein }) => {
        // Tortilla Stress Model v3.7.2
        let stressLoad = 0;
        stressLoad += { normal: 1, extra: 2.5, none: 0 }[rice];
        stressLoad += { normal: 1, extra: 2.5, none: 0 }[beans];
        stressLoad += salsas.filter((s) => s !== "none").length * 0.8;
        if (sour_cream) stressLoad += 1.2;
        if (guac) stressLoad += 1.5;
        if (double_protein) stressLoad += 2.0;
    
        // Moisture risk from liquids
        const moistureRisk = salsas.filter((s) => s !== "none").length * 0.15 + (sour_cream ? 0.2 : 0);
    
        const explosionProbability = Math.min((stressLoad / 12 + moistureRisk).toFixed(2), 0.99);
    
        let verdict, recommendation;
        if (explosionProbability > 0.75) {
          verdict = "CRITICAL";
          recommendation = "switch_to_bowl";
        } else if (explosionProbability > 0.5) {
          verdict = "WARNING";
          recommendation = "eat_over_plate";
        } else if (explosionProbability > 0.3) {
          verdict = "CAUTION";
          recommendation = "extra_napkins";
        } else {
          verdict = "STABLE";
          recommendation = "proceed_with_confidence";
        }
    
        const lines = [
          "# Burrito Integrity Report",
          "",
          "```",
          "  Tortilla Stress Analysis Engine v3.7.2",
          "  =======================================",
          `  Structural Load:       ${stressLoad.toFixed(1)} / 12.0 TSU`,
          `  Moisture Risk:         ${(moistureRisk * 100).toFixed(0)}%`,
          `  Explosion Probability: ${(explosionProbability * 100).toFixed(0)}%`,
          `  Integrity Verdict:     ${verdict}`,
          "```",
          "",
          `**Recommendation:** ${recommendation.replace(/_/g, " ")}`,
          "",
          ...(explosionProbability > 0.75
            ? [
                "> ALERT: Tortilla failure is near-certain. Multiple structural risk factors detected.",
                "> Consider a bowl. Your shirt will thank you.",
              ]
            : []),
          ...(explosionProbability <= 0.3
            ? ["> This burrito is aerodynamically sound. Godspeed."]
            : []),
        ];
    
        return { content: [{ type: "text", text: lines.join("\n") }] };
      }
    );
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full behavioral disclosure burden. It effectively explains the methodology ('proprietary tortilla stress models') and the prediction target ('structurally fail'). However, it omits crucial execution details: what the output format is (boolean, probability score, failure mode description?), whether this requires network calls, and any rate limits or side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence with zero waste. 'Proprietary tortilla stress models' establishes domain expertise, 'structurally fail' defines the risk category, and 'during consumption' specifies the temporal scope. Every phrase adds unique semantic value beyond the tool name.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the whimsical domain, the description adequately explains the physics-based prediction purpose. With 6 well-documented input parameters but no output schema, it successfully covers the 'what it does' aspect but could benefit from a brief note on return value format (integrity score vs. pass/fail) to be fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with all 6 parameters fully documented in the schema. The description does not mention parameters, but with such high schema coverage, the baseline expectation is met. The description focuses on the prediction capability rather than re-documenting ingredient parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description uses specific verb 'predict' and clarifies the exact resource domain (burrito structural integrity via 'tortilla stress models'). It clearly distinguishes from siblings like 'salsa_risk_assessment' (spice/ingredient risk) and 'bowl_vs_burrito_decision_engine' by focusing specifically on physical structural failure during consumption.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies this is a pre-consumption check ('during consumption'), suggesting when to use it. However, it lacks explicit guidance on when to prefer this over 'build_entree' (which presumably validates ingredients) or whether this should be called before or after building the order. No alternative tools are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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